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Estimation of Insurance Investment Risk Using Value-at-Risk

Abstract

The study examines the estimation of insurance investment risk using Value-at-Risk (VaR), with emphasis on the application of quantitative risk measurement techniques to assess potential losses arising from fluctuations in insurance investment portfolios. Insurance companies invest substantial funds in financial assets to generate investment income and support future policyholder obligations. However, changes in market prices, interest rates, exchange rates, and other financial conditions can expose insurers to investment losses. Accurate measurement of these risks is therefore essential for effective portfolio management and financial stability. The study will investigate the effectiveness of Value-at-Risk in estimating the potential level of loss that an insurance investment portfolio may experience over a specified time horizon and at a given confidence level. It will examine the risk exposure associated with selected insurance investment assets and assess the extent to which VaR can provide useful information for investment risk management. Attention will be given to the relationship between portfolio returns, market volatility, and estimated potential losses. The study will consider major approaches to VaR estimation, including the historical simulation method, variance-covariance method, and Monte Carlo simulation. These approaches will be applied to assess potential losses under different market conditions and confidence levels. The study will also consider factors such as asset price volatility, portfolio composition, investment horizon, and market movements in determining the estimated Value-at-Risk of insurance investment portfolios. A quantitative research approach will be adopted for the study. Historical investment return data for selected insurance assets will be obtained from appropriate financial and insurance sources. Descriptive statistics, volatility analysis, return distribution analysis, and Value-at-Risk estimation techniques will be employed to determine potential investment losses. Where appropriate, different VaR estimation methods will be compared to assess their consistency and effectiveness in measuring insurance investment risk. The study is expected to reveal that Value-at-Risk provides a useful quantitative measure for estimating potential losses associated with insurance investment portfolios. The findings may show that estimated risk levels vary according to the composition and volatility of investment assets, with portfolios containing more volatile assets generally producing higher potential loss estimates. The study may also indicate differences in VaR estimates across the alternative estimation methods, particularly where investment returns do not follow a normal distribution. The study is expected to provide useful information for insurance companies, actuaries, investment managers, regulators, and other financial stakeholders. Reliable VaR estimates may support portfolio monitoring, investment decision-making, capital planning, risk limits, and the development of appropriate risk management strategies. The findings may also assist insurers in identifying portfolios with relatively high exposure to market losses and improving their preparedness for adverse financial conditions. The study concludes that Value-at-Risk is a valuable tool for estimating and monitoring investment risk within insurance portfolios. It is therefore recommended that insurance companies incorporate appropriate VaR techniques into their investment risk management frameworks and regularly update risk estimates using current market data. The use of VaR alongside other risk measures and stress-testing techniques is also recommended to provide a broader assessment of potential investment losses and strengthen the financial resilience of insurance institutions.

Keywords: Value-at-Risk, insurance investment risk, investment portfolio, market risk, risk estimation, portfolio risk, financial risk, insurance companies, investment returns, market volatility, historical simulation, variance-covariance method, Monte Carlo simulation, actuarial risk management, investment risk management.